Abstract
A novel framework, which incorporates implicit stochastic optimization (Monte Carlo method), cluster analysis (machine learning algorithm), and Karhunen-Loeve expansion (dimension reduction technique) is proposed. The framework aims to train a Genetic Algorithm-based optimization model with synthetic and/or historical data) in an offline environment in order to develop a transformed model for the online optimization (i.e., real-time optimization). The primary output from the offline training is a stochastic representation of the decision variables that are constituted by a series of orthogonal functions with undetermined random coefficients. This representation preserves covariance structure of the simulated decisions from the offline training as gains some “knowledge” regarding the search space. Due to this gained “knowledge”, better candidate solutions can be generated and hence, the optimal solutions can be obtained faster. The feasibility of the approach is demonstrated with a case study for optimizing hourly operation of a ten-reservoir system during a two-week period.
Published Version
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